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The Statistical Crisis in Science

73 点作者 jonathansizz超过 10 年前

8 条评论

yummyfajitas超过 10 年前
So at least two people reading this seem to think it&#x27;s about using science in the context of their pet peeves. It&#x27;s not.<p>It&#x27;s about using a statistical test for a data dependent hypothesis and interpreting the test as if it were used for a data-independent hypothesis. That&#x27;s all.<p>It&#x27;s not about using statistics in politics or finance. It&#x27;s about first looking at the data, then formulating a hypothesis, then running a standard test which is based on the idea that you chose the hypothesis independently of the data. This is a problem in any field.
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dschiptsov超过 10 年前
Not only errors and misuse of statistics and misapplying of probability theory, but also abstract modeling in general.<p>The very idea of modeling dynamic abstract processes such as finance markets, which itself are mere abstractions is a non-science, it is misuse of pseudo-scientific methods and mathematics, and what we have seen so far is nothing but failures.<p>Too abstract or flawed abstractions and wrong premises cannot be fixed by any amount of math or modeling. They only has to be discarded.<p>The famous &quot;subject&#x2F;object&quot; false dichotomy in philosophy is the good example too. People could spent ages modeling reality using non-existent abstractions.<p>Today all these multiverse &quot;theories&quot; are mere speculations about whether Siva, Brama or Visnu is the most powerful, forgetting that all these were nothing but anthropomorphic abstractions of the different aspects of one reality.<p>The notion that so-called &quot;modern science&quot; is a new religion (a contest of unproven speculations) is already quite old.<p>btw, a good example of the reductionist mindset (instead of pilling up abstractions) could be the Upanishadic reduction of all the Gods to one Brahman, to which Einstein accidentally discovered a formula - E = mc2, where <i>c</i> is a constant, implying that there is no time in the Universe).
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chriswarbo超过 10 年前
Scientists have tried over at least the past few hundred years (depending on your definitions) to build, from scratch, a perspective on the world which is as free from human bias as possible. At the moment, the jewel in the crown is quantum physics: an inherently statistical theory, so detached from human biases and assumptions that many smart people have struggled to understand or accept it, despite its incredible predictive power.<p>At the heart of the whole process is statistical inference: generalising the results of experiments or observations to the Universe as a whole. A &quot;statistical crisis in science&quot; would be terrible news. We may have been standing on the shoulders of the misinformed, rather than giants. Our &quot;achievements&quot;, from particle accelerators to nukes and moon rockets, could have been flukes; if the underlying statistical approach of science was flawed, the predicted behaviour and safety margins of these devices could have been way off. We may be routinely bringing the world to the edge of catastrophe, if we don&#x27;t understand the consequences of our actions.<p>Oh wait, it seems like some &quot;political scientists&quot; have noticed that their results tend to be influenced by external factors. I hope they realise the irony in their choice of examples:<p>&gt; As a hypothetical example, suppose a researcher is interested in how Democrats and Republicans perform differently in a short mathematics test when it is expressed in two different contexts, involving either healthcare or the military.<p>The article criticises scientists&#x27; ability to navigate the statistical minefield of biases, probability estimates, modelling assumptions, etc. in a world of external, political factors like competitive funding, positive publication bias, etc. and they choose an example of <i>measuring how political factors affect people&#x27;s math skills</i>!<p>To me, that seems the sociological equivalent of trying to measure the thermal expansion of a ruler by reading its markings. What do you know, it&#x27;s still 30cm!
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chuckcode超过 10 年前
&quot;all models are wrong, but some are useful.&quot; - George Box [1]<p>George Box expressed early my general feeling about statistics, it is a very useful tool but remember the limitations of the methods, the data, and the people applying them. I would like to seen an emphasis on openness and transparency with data so others can replicate the analysis and the community can come up with ways to make best practices accessible to anyone.<p>[1] <a href="http://en.wikiquote.org/wiki/George_E._P._Box" rel="nofollow">http:&#x2F;&#x2F;en.wikiquote.org&#x2F;wiki&#x2F;George_E._P._Box</a>
SaberTail超过 10 年前
A good (in my opinion) trend in physics in the past decade or two has been the rise of &quot;blind&quot; analyses[1]. Basically, the entire analysis is predetermined, before looking at the data. Once all the details are nailed down and everyone agrees with the approach, the blinds are taken off. There&#x27;s no room for &quot;p-hacking&quot;.<p>This has some disadvantages, though. It requires a good understanding of the experiment so that you can figure out what an analysis will actually tell you. It&#x27;s difficult to do a blind analysis on a brand new apparatus, since there can always be unanticipated problems with the data. As an example, one dark matter experiment invited a reporter to their unblinding. At first, it looked like they&#x27;d detected dark matter, but then they had to throw out most of the events because they were due to unanticipated noise in one of the photomultiplier tubes[2].<p>[1] <a href="http://www.slac.stanford.edu/econf/C030908/papers/TUIT001.pdf" rel="nofollow">http:&#x2F;&#x2F;www.slac.stanford.edu&#x2F;econf&#x2F;C030908&#x2F;papers&#x2F;TUIT001.pd...</a> is a quick review.<p>[2] <a href="http://www.nytimes.com/2011/04/14/science/space/14dark.html" rel="nofollow">http:&#x2F;&#x2F;www.nytimes.com&#x2F;2011&#x2F;04&#x2F;14&#x2F;science&#x2F;space&#x2F;14dark.html</a>
amelius超过 10 年前
I really don&#x27;t understand the meaning of this sentence (below). Perhaps somebody could explain?<p>&gt; As a hypothetical example, suppose a researcher is interested in how Democrats and Republicans perform differently in a short mathematics test when it is expressed in two different contexts, involving either healthcare or the military.
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jmmcd超过 10 年前
&gt; In general, p-values are based on what would have happened under other possible data sets. As a hypothetical example, suppose a researcher is interested in how Democrats and Republicans perform differently in a short mathematics test when it is expressed in two different contexts, involving either healthcare or the military. [...] At this point a huge number of possible comparisons could be performed, all consistent with the researcher’s theory. For example, the null hypothesis could be rejected (with statistical significance) among men and not among women—explicable under the theory that men are more ideological than women.<p>The meaning of a p-value is expressed in terms of what would have happened with a different data set, yes, but that different data set would have arisen through a different random sampling from the population. The explanation above seems to completely misunderstand the issue.
CurtMonash超过 10 年前
Between the failings in statistics and those in modeling, there&#x27;s a whole lot of science that&#x27;s on shaky ground.